Summer Sessions | Courses | Quantitative Methods: Social Sciences

Quantitative Methods: Social Sciences

Check the Directory of Classes for the most up-to-date course information.

Summer 2022 Session Information

  • SESSION A (First Half Term) courses are May 23–July 1, 2022
  • SESSION B (Second Half Term) courses are July 5–August 12, 2022
  • SESSION X (Full Term) courses are May 23–August 12, 2022
Courses
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DATA ANALYSIS WITH PYTHON
QMSS5019S001 3 points.

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.

Course Number Section/Call Number Session Times/Location
QMSS5019S001 001/10319 Session A Mo 09:00 AM–12:10 PM
We 09:00 AM–12:10 PM

Instructor Points Enrollment Method of Instruction
Gregory Eirich
3 Open for Enrollment
(auto-fill Wait List)
In-Person
Entrepreneurial Principles and Quantitative Reasoning
QMSS5066G001 3 points.

This course is structured around class-wide and individual exercises that introduce students to the key principles of entrepreneurial thinking, such as identifying problems and opportunities, thinking creatively, developing minimally viable products (MVPs) and low fidelity prototypes, creating a reliable workflow, pivoting and course-correcting, finding valuable help, and developing productive habits. In addition, students are introduced to the key tools of quantitative reasoning, including surveys, observational data, experiments, simulation and projections, data analysis, statistical reasoning, organized researching, and persuasive and authoritative writing – and how those tools support entrepreneurial projects. Students should leave the class equipped with the sense that they themselves can produce constructive change in their world, along with a blueprint for how to go about getting it started.

Course Number Section/Call Number Session Times/Location
QMSS5066G001 001/10320 Session A Tu 09:00 AM–12:10 PM
Th 09:00 AM–12:10 PM

Instructor Points Enrollment Method of Instruction
Gregory Eirich
3 Open for Enrollment
(auto-fill Wait List)
In-Person
NATURAL LANG PROCESSING SOCIAL SCIENCES
QMSS5067G001 3 points.

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

Course Number Section/Call Number Session Times/Location
QMSS5067G001 001/12062 Session B Mo 05:30 PM–08:40 PM
Tu 05:30 PM–08:40 PM

Instructor Points Enrollment Method of Instruction
Patrick Houlihan
3 Open for Enrollment
(auto-fill Wait List)
In-Person
MACHINE LEARNING SOC SCI
QMSS5073S001 3 points.
Course Number Section/Call Number Session Times/Location
QMSS5073S001 001/11434 Session A Mo 01:00 PM–04:10 PM
We 01:00 PM–04:10 PM

Instructor Points Enrollment Method of Instruction
Michael Parrott
3 Open for Enrollment
(auto-fill Wait List)
In-Person
INDEPENDENT STUDY
QMSS5997G001 4 points.

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.

Course Number Section/Call Number Session Times/Location
QMSS5997G001 001/11355 Session A
Instructor Points Enrollment Method of Instruction
Gregory Eirich
4 Open for Enrollment
(auto-fill Wait List)
In-Person